In [1]:
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio
import plotly.colors as colors
pio.templates.default = "plotly_white"
--------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) Cell In[1], line 3 1 import pandas as pd ----> 3 import plotly.express as px 4 import plotly.graph_objects as go 5 import plotly.io as pio ModuleNotFoundError: No module named 'plotly'
In [2]:
!pip install plotly
Collecting plotly Downloading plotly-6.5.0-py3-none-any.whl.metadata (8.5 kB) Collecting narwhals>=1.15.1 (from plotly) Downloading narwhals-2.14.0-py3-none-any.whl.metadata (13 kB) Requirement already satisfied: packaging in c:\users\pmani\appdata\local\programs\python\python313\lib\site-packages (from plotly) (25.0) Downloading plotly-6.5.0-py3-none-any.whl (9.9 MB) ---------------------------------------- 0.0/9.9 MB ? eta -:--:-- - -------------------------------------- 0.3/9.9 MB ? eta -:--:-- ---- ----------------------------------- 1.0/9.9 MB 2.9 MB/s eta 0:00:04 ------ --------------------------------- 1.6/9.9 MB 2.9 MB/s eta 0:00:03 -------- ------------------------------- 2.1/9.9 MB 2.9 MB/s eta 0:00:03 ----------- ---------------------------- 2.9/9.9 MB 2.9 MB/s eta 0:00:03 ------------- -------------------------- 3.4/9.9 MB 2.9 MB/s eta 0:00:03 --------------- ------------------------ 3.9/9.9 MB 2.9 MB/s eta 0:00:03 ------------------ --------------------- 4.5/9.9 MB 2.9 MB/s eta 0:00:02 --------------------- ------------------ 5.2/9.9 MB 2.9 MB/s eta 0:00:02 ----------------------- ---------------- 5.8/9.9 MB 2.9 MB/s eta 0:00:02 ------------------------- -------------- 6.3/9.9 MB 2.9 MB/s eta 0:00:02 --------------------------- ------------ 6.8/9.9 MB 2.9 MB/s eta 0:00:02 ------------------------------ --------- 7.6/9.9 MB 2.9 MB/s eta 0:00:01 ------------------------------- -------- 7.9/9.9 MB 2.9 MB/s eta 0:00:01 ---------------------------------- ----- 8.7/9.9 MB 2.8 MB/s eta 0:00:01 ------------------------------------- -- 9.2/9.9 MB 2.8 MB/s eta 0:00:01 ---------------------------------------- 9.9/9.9 MB 2.8 MB/s 0:00:03 Downloading narwhals-2.14.0-py3-none-any.whl (430 kB) Installing collected packages: narwhals, plotly ---------------------------------------- 0/2 [narwhals] ---------------------------------------- 0/2 [narwhals] ---------------------------------------- 0/2 [narwhals] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] -------------------- ------------------- 1/2 [plotly] ---------------------------------------- 2/2 [plotly] Successfully installed narwhals-2.14.0 plotly-6.5.0
[notice] A new release of pip is available: 25.2 -> 25.3 [notice] To update, run: python.exe -m pip install --upgrade pip
In [27]:
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio
import plotly.colors as colors
pio.templates.default = "plotly_white"
pio.renderers.default = "notebook"
In [28]:
data = pd.read_csv("Sample - Superstore.csv" , encoding = 'latin-1')
In [29]:
data.head()
Out[29]:
| Row ID | Order ID | Order Date | Ship Date | Ship Mode | Customer ID | Customer Name | Segment | Country | City | ... | Postal Code | Region | Product ID | Category | Sub-Category | Product Name | Sales | Quantity | Discount | Profit | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | CA-2016-152156 | 11/8/2016 | 11/11/2016 | Second Class | CG-12520 | Claire Gute | Consumer | United States | Henderson | ... | 42420 | South | FUR-BO-10001798 | Furniture | Bookcases | Bush Somerset Collection Bookcase | 261.9600 | 2 | 0.00 | 41.9136 |
| 1 | 2 | CA-2016-152156 | 11/8/2016 | 11/11/2016 | Second Class | CG-12520 | Claire Gute | Consumer | United States | Henderson | ... | 42420 | South | FUR-CH-10000454 | Furniture | Chairs | Hon Deluxe Fabric Upholstered Stacking Chairs,... | 731.9400 | 3 | 0.00 | 219.5820 |
| 2 | 3 | CA-2016-138688 | 6/12/2016 | 6/16/2016 | Second Class | DV-13045 | Darrin Van Huff | Corporate | United States | Los Angeles | ... | 90036 | West | OFF-LA-10000240 | Office Supplies | Labels | Self-Adhesive Address Labels for Typewriters b... | 14.6200 | 2 | 0.00 | 6.8714 |
| 3 | 4 | US-2015-108966 | 10/11/2015 | 10/18/2015 | Standard Class | SO-20335 | Sean O'Donnell | Consumer | United States | Fort Lauderdale | ... | 33311 | South | FUR-TA-10000577 | Furniture | Tables | Bretford CR4500 Series Slim Rectangular Table | 957.5775 | 5 | 0.45 | -383.0310 |
| 4 | 5 | US-2015-108966 | 10/11/2015 | 10/18/2015 | Standard Class | SO-20335 | Sean O'Donnell | Consumer | United States | Fort Lauderdale | ... | 33311 | South | OFF-ST-10000760 | Office Supplies | Storage | Eldon Fold 'N Roll Cart System | 22.3680 | 2 | 0.20 | 2.5164 |
5 rows × 21 columns
In [30]:
data.describe()
Out[30]:
| Row ID | Postal Code | Sales | Quantity | Discount | Profit | |
|---|---|---|---|---|---|---|
| count | 9994.000000 | 9994.000000 | 9994.000000 | 9994.000000 | 9994.000000 | 9994.000000 |
| mean | 4997.500000 | 55190.379428 | 229.858001 | 3.789574 | 0.156203 | 28.656896 |
| std | 2885.163629 | 32063.693350 | 623.245101 | 2.225110 | 0.206452 | 234.260108 |
| min | 1.000000 | 1040.000000 | 0.444000 | 1.000000 | 0.000000 | -6599.978000 |
| 25% | 2499.250000 | 23223.000000 | 17.280000 | 2.000000 | 0.000000 | 1.728750 |
| 50% | 4997.500000 | 56430.500000 | 54.490000 | 3.000000 | 0.200000 | 8.666500 |
| 75% | 7495.750000 | 90008.000000 | 209.940000 | 5.000000 | 0.200000 | 29.364000 |
| max | 9994.000000 | 99301.000000 | 22638.480000 | 14.000000 | 0.800000 | 8399.976000 |
In [31]:
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 9994 entries, 0 to 9993 Data columns (total 21 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Row ID 9994 non-null int64 1 Order ID 9994 non-null object 2 Order Date 9994 non-null object 3 Ship Date 9994 non-null object 4 Ship Mode 9994 non-null object 5 Customer ID 9994 non-null object 6 Customer Name 9994 non-null object 7 Segment 9994 non-null object 8 Country 9994 non-null object 9 City 9994 non-null object 10 State 9994 non-null object 11 Postal Code 9994 non-null int64 12 Region 9994 non-null object 13 Product ID 9994 non-null object 14 Category 9994 non-null object 15 Sub-Category 9994 non-null object 16 Product Name 9994 non-null object 17 Sales 9994 non-null float64 18 Quantity 9994 non-null int64 19 Discount 9994 non-null float64 20 Profit 9994 non-null float64 dtypes: float64(3), int64(3), object(15) memory usage: 1.6+ MB
Converting date columns¶
In [32]:
data['Order Date'] = pd.to_datetime(data['Order Date'])
In [33]:
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 9994 entries, 0 to 9993 Data columns (total 21 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Row ID 9994 non-null int64 1 Order ID 9994 non-null object 2 Order Date 9994 non-null datetime64[ns] 3 Ship Date 9994 non-null object 4 Ship Mode 9994 non-null object 5 Customer ID 9994 non-null object 6 Customer Name 9994 non-null object 7 Segment 9994 non-null object 8 Country 9994 non-null object 9 City 9994 non-null object 10 State 9994 non-null object 11 Postal Code 9994 non-null int64 12 Region 9994 non-null object 13 Product ID 9994 non-null object 14 Category 9994 non-null object 15 Sub-Category 9994 non-null object 16 Product Name 9994 non-null object 17 Sales 9994 non-null float64 18 Quantity 9994 non-null int64 19 Discount 9994 non-null float64 20 Profit 9994 non-null float64 dtypes: datetime64[ns](1), float64(3), int64(3), object(14) memory usage: 1.6+ MB
In [34]:
data['Ship Date'] = pd.to_datetime(data['Ship Date'])
In [35]:
data.head()
Out[35]:
| Row ID | Order ID | Order Date | Ship Date | Ship Mode | Customer ID | Customer Name | Segment | Country | City | ... | Postal Code | Region | Product ID | Category | Sub-Category | Product Name | Sales | Quantity | Discount | Profit | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | CA-2016-152156 | 2016-11-08 | 2016-11-11 | Second Class | CG-12520 | Claire Gute | Consumer | United States | Henderson | ... | 42420 | South | FUR-BO-10001798 | Furniture | Bookcases | Bush Somerset Collection Bookcase | 261.9600 | 2 | 0.00 | 41.9136 |
| 1 | 2 | CA-2016-152156 | 2016-11-08 | 2016-11-11 | Second Class | CG-12520 | Claire Gute | Consumer | United States | Henderson | ... | 42420 | South | FUR-CH-10000454 | Furniture | Chairs | Hon Deluxe Fabric Upholstered Stacking Chairs,... | 731.9400 | 3 | 0.00 | 219.5820 |
| 2 | 3 | CA-2016-138688 | 2016-06-12 | 2016-06-16 | Second Class | DV-13045 | Darrin Van Huff | Corporate | United States | Los Angeles | ... | 90036 | West | OFF-LA-10000240 | Office Supplies | Labels | Self-Adhesive Address Labels for Typewriters b... | 14.6200 | 2 | 0.00 | 6.8714 |
| 3 | 4 | US-2015-108966 | 2015-10-11 | 2015-10-18 | Standard Class | SO-20335 | Sean O'Donnell | Consumer | United States | Fort Lauderdale | ... | 33311 | South | FUR-TA-10000577 | Furniture | Tables | Bretford CR4500 Series Slim Rectangular Table | 957.5775 | 5 | 0.45 | -383.0310 |
| 4 | 5 | US-2015-108966 | 2015-10-11 | 2015-10-18 | Standard Class | SO-20335 | Sean O'Donnell | Consumer | United States | Fort Lauderdale | ... | 33311 | South | OFF-ST-10000760 | Office Supplies | Storage | Eldon Fold 'N Roll Cart System | 22.3680 | 2 | 0.20 | 2.5164 |
5 rows × 21 columns
In [36]:
data['Order Month'] = data['Order Date'].dt.month
data['Order Year'] = data['Order Date'].dt.year
data['Order Day of Week'] = data['Order Date'].dt.dayofweek
In [37]:
data.head()
Out[37]:
| Row ID | Order ID | Order Date | Ship Date | Ship Mode | Customer ID | Customer Name | Segment | Country | City | ... | Category | Sub-Category | Product Name | Sales | Quantity | Discount | Profit | Order Month | Order Year | Order Day of Week | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | CA-2016-152156 | 2016-11-08 | 2016-11-11 | Second Class | CG-12520 | Claire Gute | Consumer | United States | Henderson | ... | Furniture | Bookcases | Bush Somerset Collection Bookcase | 261.9600 | 2 | 0.00 | 41.9136 | 11 | 2016 | 1 |
| 1 | 2 | CA-2016-152156 | 2016-11-08 | 2016-11-11 | Second Class | CG-12520 | Claire Gute | Consumer | United States | Henderson | ... | Furniture | Chairs | Hon Deluxe Fabric Upholstered Stacking Chairs,... | 731.9400 | 3 | 0.00 | 219.5820 | 11 | 2016 | 1 |
| 2 | 3 | CA-2016-138688 | 2016-06-12 | 2016-06-16 | Second Class | DV-13045 | Darrin Van Huff | Corporate | United States | Los Angeles | ... | Office Supplies | Labels | Self-Adhesive Address Labels for Typewriters b... | 14.6200 | 2 | 0.00 | 6.8714 | 6 | 2016 | 6 |
| 3 | 4 | US-2015-108966 | 2015-10-11 | 2015-10-18 | Standard Class | SO-20335 | Sean O'Donnell | Consumer | United States | Fort Lauderdale | ... | Furniture | Tables | Bretford CR4500 Series Slim Rectangular Table | 957.5775 | 5 | 0.45 | -383.0310 | 10 | 2015 | 6 |
| 4 | 5 | US-2015-108966 | 2015-10-11 | 2015-10-18 | Standard Class | SO-20335 | Sean O'Donnell | Consumer | United States | Fort Lauderdale | ... | Office Supplies | Storage | Eldon Fold 'N Roll Cart System | 22.3680 | 2 | 0.20 | 2.5164 | 10 | 2015 | 6 |
5 rows × 24 columns
Monthly Sales Analysis¶
In [38]:
sales_by_month = data.groupby('Order Month')['Sales'].sum().reset_index()
fig = px.line(sales_by_month,
x = 'Order Month',
y = 'Sales',
title = 'Monthly Sales Analysis')
fig.show()
Sales by Category¶
In [39]:
sales_by_category = data.groupby('Category')['Sales'].sum().reset_index()
In [40]:
sales_by_category
Out[40]:
| Category | Sales | |
|---|---|---|
| 0 | Furniture | 741999.7953 |
| 1 | Office Supplies | 719047.0320 |
| 2 | Technology | 836154.0330 |
In [43]:
fig = px.pie(sales_by_category,
values = 'Sales',
names = 'Category',
hole = 0.5,
color_discrete_sequence = px.colors.qualitative.Pastel)
fig.update_traces(textposition='inside',textinfo='percent+label')
fig.update_layout(title_text='Sales Analysis by Category',title_font=dict(size=24))
fig.show()
Sales Analysis by Sub-Category¶
In [44]:
sales_by_subcategory = data.groupby('Sub-Category')['Sales'].sum().reset_index()
In [45]:
sales_by_subcategory
Out[45]:
| Sub-Category | Sales | |
|---|---|---|
| 0 | Accessories | 167380.3180 |
| 1 | Appliances | 107532.1610 |
| 2 | Art | 27118.7920 |
| 3 | Binders | 203412.7330 |
| 4 | Bookcases | 114879.9963 |
| 5 | Chairs | 328449.1030 |
| 6 | Copiers | 149528.0300 |
| 7 | Envelopes | 16476.4020 |
| 8 | Fasteners | 3024.2800 |
| 9 | Furnishings | 91705.1640 |
| 10 | Labels | 12486.3120 |
| 11 | Machines | 189238.6310 |
| 12 | Paper | 78479.2060 |
| 13 | Phones | 330007.0540 |
| 14 | Storage | 223843.6080 |
| 15 | Supplies | 46673.5380 |
| 16 | Tables | 206965.5320 |
In [46]:
fig = px.bar(sales_by_subcategory,
x = 'Sub-Category',
y = 'Sales',
title = "Sales analysis by sub category")
fig.show()
Monthly Profit Analysis¶
In [47]:
profit_by_month = data.groupby('Order Month')['Profit'].sum().reset_index()
In [48]:
profit_by_month
Out[48]:
| Order Month | Profit | |
|---|---|---|
| 0 | 1 | 9134.4461 |
| 1 | 2 | 10294.6107 |
| 2 | 3 | 28594.6872 |
| 3 | 4 | 11587.4363 |
| 4 | 5 | 22411.3078 |
| 5 | 6 | 21285.7954 |
| 6 | 7 | 13832.6648 |
| 7 | 8 | 21776.9384 |
| 8 | 9 | 36857.4753 |
| 9 | 10 | 31784.0413 |
| 10 | 11 | 35468.4265 |
| 11 | 12 | 43369.1919 |
In [49]:
fig = px.line(profit_by_month,
x = 'Order Month',
y = 'Profit',
title = "Monthly profit analysis")
fig.show()
Profit by Category¶
In [50]:
profit_by_category = data.groupby('Category')['Profit'].sum().reset_index()
In [51]:
profit_by_category
Out[51]:
| Category | Profit | |
|---|---|---|
| 0 | Furniture | 18451.2728 |
| 1 | Office Supplies | 122490.8008 |
| 2 | Technology | 145454.9481 |
In [53]:
fig = px.pie(profit_by_category,
values = 'Profit',
names = 'Category',
hole = 0.5,
color_discrete_sequence = px.colors.qualitative.Pastel)
fig.update_traces(textposition = 'inside', textinfo = 'percent+label')
fig.update_layout(title_text = 'Profit Analysis by Category', title_font = dict(size=24))
fig.show()
Profit by Sub Category¶
In [54]:
profit_by_subcategory = data.groupby('Sub-Category')['Profit'].sum().reset_index()
fig = px.bar(profit_by_subcategory,
x = 'Sub-Category',
y = 'Profit',
title = "Profit Analysis by Sub-Category")
fig.show()
Sales and Profit - Customer Segment¶
In [55]:
sales_profit_by_segment = data.groupby('Segment').agg({'Sales':'sum','Profit':'sum'}).reset_index()
color_palette = colors.qualitative.Pastel
fig = go.Figure()
fig.add_trace(go.Bar(x = sales_profit_by_segment['Segment'],
y = sales_profit_by_segment['Sales'],
name = 'Sales',
marker_color = color_palette[0]))
fig.add_trace(go.Bar(x = sales_profit_by_segment['Segment'],
y = sales_profit_by_segment['Profit'],
name = 'Profit',
marker_color = color_palette[1]))
fig.update_layout(title = 'Sales and Profit Analysis by Customer Segment',
xaxis_title = 'Customer_segment',yaxis_title = 'Amount')
fig.show()
Sales to Profit ratio¶
In [56]:
sales_profit_by_segment = data.groupby('Segment').agg({'Sales':'sum','Profit':'sum'}).reset_index()
sales_profit_by_segment['Sales_to_Profit_Ratio'] = sales_profit_by_segment['Sales']/sales_profit_by_segment['Profit']
print(sales_profit_by_segment[['Segment','Sales_to_Profit_Ratio']])
Segment Sales_to_Profit_Ratio 0 Consumer 8.659471 1 Corporate 7.677245 2 Home Office 7.125416
In [ ]: